Authors:
Ying Sha
and
Jianlong Tan
Affiliation:
The Software Division, Institute of Computing Technology, Chinese Academy of Sciences, China
Keyword(s):
Max/Min, Sliding Window, Data Streams.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Business Analytics
;
Communication and Software Technologies and Architectures
;
Data Engineering
;
Data Management and Quality
;
Data Warehouses and Data Mining
;
e-Business
;
Enterprise Information Systems
;
Information Quality
;
Information Retrieval
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Software Engineering
Abstract:
With the development of Internet, more and more data-stream based applications emerged, where calculation of aggregate functions plays an important role. Many studies were conducted on aggregation functions; however, an efficient algorithm to calculate Max/Min values remains an open problem. Here, we propose a novel, exact method to computer Max/Min values for the numerical input data. Employing an incrementally calculating strategy on sliding windows, this algorithm gains a high efficiency. We analyze the algorithm and prove the time-complexity and space-complexity in worst cases. Experimental results confirm its high performance on a testing dataset.